remotes::install_github("nsgrantham/ggbraid")1 The task
In this take-home exercise, you are required to uncover the impact of COVID-19 as well as the global economic and political dynamic in 2022 on Singapore bi-lateral trade (i.e. Import, Export and Trade Balance) by using appropriate analytical visualisation techniques learned in Lesson 6: It’s About Time. Students are encouraged to apply appropriate interactive techniques to enhance user and data discovery experiences.
2 Introduction
In 2020, Singapore was the 36th largest economy in terms of GDP, the 19th largest in terms of total exports, the 17th largest in terms of total imports, the 11th largest in terms of GDP per capita, and the sixth most complex economy according to the Economic Complexity Index (ECI).
Since 2016, Singapore has primarily exported to Hong Kong, China, Malaysia, the United States, and Indonesia, while the majority of its imports have originated from China, Malaysia, Taiwan, the United States, and Japan.
The increasing contribution of import/export rate reflects the significance of foreign trade to Singapore’s economy. Prior to the outbreak of COVID-19 in 2020, Singapore’s bilateral trade growth between 2010 and 2019 was robust. Importantly, during this time period, export growth continuously outperformed that of bilateral commerce. Hence, the value of exports increased consistently from 2016 to 2019.
Using Time-series visualization tools, this article will reveal the impact of COVID-19 on the global economic and political dynamic in 2022.
Packages
The following packages are used for this project.
- tidyverse:
- readxl: This package makes it easy to get data out of Excel and into R. It has no external dependencies, so it’s easy to install and use on all operating systems. It is designed to work with tabular data. The easiest way to install the latest released version from CRAN is to install the whole tidyverse. Note that you will still need to load readxl explicitly, because it is not a core tidyverse package loaded via
library(tidyverse) - tidyr: Tidy data describes a standard way of storing data that is used wherever possible throughout the tidyverse. If you ensure that your data is tidy, you’ll spend less time fighting with the tools and more time working on your analysis.
- lubridate: Functions to work with date-times and time-spans: fast and user friendly parsing of date-time data, extraction and updating of components of a date-time (years, months, days, hours, minutes, and seconds), algebraic manipulation on date-time and time-span objects. The easiest way to get lubridate is to install the whole tidyverse.
- readxl: This package makes it easy to get data out of Excel and into R. It has no external dependencies, so it’s easy to install and use on all operating systems. It is designed to work with tabular data. The easiest way to install the latest released version from CRAN is to install the whole tidyverse. Note that you will still need to load readxl explicitly, because it is not a core tidyverse package loaded via
- plotly: is an R package for creating interactive web-based graphs via the open source JavaScript graphing library
plotly.js. - d3scatter: is an HTML R widget for interactive scatter plots visualization. It is based on the htmlwidgets R package and on the d3.js javascript library.
- ggbraid: ggbraid provides a new stat,
stat_braid(), that extends the functionality ofgeom_ribbon()to correctly fill the area between two alternating lines (or steps) with two different colors. ggbraid also provides a geom,geom_braid(), that wrapsgeom_ribbon()and usesstat_braid()by default. You can install the development version of ggbraid from GitHub with:
ggplot2: A system for ‘declaratively’ creating graphics, based on “The Grammar of Graphics”. You provide the data, tell ‘ggplot2’ how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
- TimeSeries: This R package offers novel time series visualisations. It is based on
ggplot2and offersgeoms and pre-packaged functions for easily creating any of the offered charts. - hrbrthemes: A compilation of extra ‘ggplot2’ themes, scales and utilities, including a spell check function plot label fields and an overall emphasis on typography.
- ggiraph: is a tool that allows you to create dynamic ggplot graphs. This allows you to add tooltips, hover effects and JavaScript actions to the graphics.
- TimeSeries: This R package offers novel time series visualisations. It is based on
tmap: With the tmap package, thematic maps can be generated with great flexibility. The syntax for creating plots is similar to that of
ggplot2, but tailored to maps.CGPfunctions: Miscellaneous functions useful for teaching statistics as well as actually practicing the art. They typically are not new methods but rather wrappers around either base R or other packages. This package is used to create slope graph in this project.
Use the code chunk below to call the packages required for this project.
packages = c('readxl', 'datawizard', 'crosstalk', 'tidyr', 'lubridate','tidyverse', 'plotly', 'd3scatter','tidyquant', 'ggbraid', 'ggTimeSeries', 'CGPfunctions', 'tmap', 'ggplot2', 'hrbrthemes', 'ggiraph', 'ggrepel')
for(p in packages){
if(!require(p,character.only = T)){
install.packages(p)
}
library(p,character.only = T)
}3 Data Preparation
Merchandise Trade provided by Department of Statistics, Singapore (DOS) is used. Pay attention to the fact that the data is in xlsx format with several tabs. As a result, we will manage using functions from the readxl packages.
Step 1. Checking the number of sheets it contains
To list all sheets in an excel spreadsheet, we utilize the readxl package’s function excel sheet.
excel_sheets("data/data.xlsx")[1] "Content" "T1" "T2" Step 2. Importing data
The readxl package’s read_xlsx() function is used in the code chunk below to import the data worksheet of our data workbook into R.
T1 <- read_xlsx("data/data.xlsx", sheet = "T1")
T2 <- read_xlsx("data/data.xlsx", sheet = "T2")Step 3. Transpose the fat table to long table
Gather takes multiple columns and collapses into key-value pairs, duplicating all other columns as needed. You use gather() when you notice that you have columns that are not variables. This function is under tidyr package.
T1 <- gather(T1, "MonthYear", "ImportValue", -`Data Series`)
T2 <- gather(T2, "MonthYear", "ExportValue", -`Data Series`)| Data Series | MonthYear | ImportValue |
|---|---|---|
| Total Merchandise Imports (Thousand Dollars) | 2022 Dec | 49869770 |
| America (Million Dollars) | 2022 Dec | 6901.5 |
| Asia (Million Dollars) | 2022 Dec | 33611.7 |
| Europe (Million Dollars) | 2022 Dec | 7541.8 |
| Oceania (Million Dollars) | 2022 Dec | 1399.9 |
| Data Series | MonthYear | ExportValue |
|---|---|---|
| Total Merchandise Exports (Thousand Dollars) | 2022 Dec | 55000084 |
| America (Million Dollars) | 2022 Dec | 6217.5 |
| Asia (Million Dollars) | 2022 Dec | 39734.8 |
| Europe (Million Dollars) | 2022 Dec | 4924.4 |
| Oceania (Million Dollars) | 2022 Dec | 3034.8 |
Step 4. Convert MonthYear column to date format
For our time series analysis, we then convert the date time to the date format using the ym function from the lubridate package.
T1$`MonthYear` <- ym(T1$`MonthYear`)
T2$`MonthYear` <- ym(T2$`MonthYear`)
# Convert ImportValue column to numeric format
T1$`ImportValue` <- as.numeric(T1$`ImportValue`)
T2$`ExportValue` <- as.numeric(T2$`ExportValue`)Step 5. Separate region and country
Notice that the country column contains both country and region-level information. To prevent misleading analysis, we have created the field Level to distinguish between the Region and Country levels.
# =================== Import =================== #
Region <- T1 %>%
filter(grepl('Million', `Data Series`)) %>%
rename("Region" = "ImportValue")
Country <- T1 %>%
filter(grepl('Thousand', `Data Series`)) %>%
rename("Country" = "ImportValue")
Import <- full_join(Region, Country, by = join_by(`Data Series`, `MonthYear`))
Import <- gather(Import , "Level", "ImportValue", -`Data Series`, -`MonthYear`)
# =================== Export =================== #
Region <- T2 %>%
filter(grepl('Million', `Data Series`)) %>%
rename("Region" = "ExportValue")
Country <- T2 %>%
filter(grepl('Thousand', `Data Series`)) %>%
rename("Country" = "ExportValue")
Export <- full_join(Region, Country, by = join_by(`Data Series`, `MonthYear`))
Export <- gather(Export , "Level", "ExportValue", -`Data Series`, -`MonthYear`)Step 6. Filter year from 2016 and rename column
Because we are focusing on the impact of the Covid epidemic event, we only filter data from 2016 to 2022.
Import <- Import %>%
filter(`MonthYear`> as.Date("2015-12-01")) %>%
rename(`Country` = `Data Series`)
Export <- Export %>%
filter(`MonthYear`> as.Date("2015-12-01")) %>%
rename(`Country` = `Data Series`)Step 7. Merge Import and Export into one table
wide <- full_join(Import, Export, by = join_by(`Country`, `MonthYear`,`Level`))
wide <- wide %>%
mutate("Diff" = ImportValue-ExportValue) %>%
mutate("Total" = ImportValue+ExportValue)
wide$`Country` <- str_replace(wide$`Country`, "Mainland China", "China")
wide$`Country` <- str_replace_all(wide$`Country`, " \\(|Thousand Dollars|\\)", "")
wide$`Country` <- str_replace_all(wide$`Country`, " \\(|Million Dollars|\\)", "")
long <- gather(wide , "Type", "Value", -`Country`, -`MonthYear`,-`Level`)And now we have both a wide table and a long table prepared for analysis.
Table Wide : Merchandise Imports/Export By Region/Market, Monthly
| Country | MonthYear | Level | ImportValue | ExportValue | Diff | Total |
|---|---|---|---|---|---|---|
| America | 2022-12-01 | Region | 6901.5 | 6217.5 | 684.0 | 13119.0 |
| Asia | 2022-12-01 | Region | 33611.7 | 39734.8 | -6123.1 | 73346.5 |
| Europe | 2022-12-01 | Region | 7541.8 | 4924.4 | 2617.4 | 12466.2 |
| Oceania | 2022-12-01 | Region | 1399.9 | 3034.8 | -1634.9 | 4434.7 |
| Africa | 2022-12-01 | Region | 414.9 | 1088.6 | -673.7 | 1503.5 |
Table Long : Merchandise Imports/Export By Region/Market, Monthly
| Country | MonthYear | Level | Type | Value |
|---|---|---|---|---|
| America | 2022-12-01 | Region | ImportValue | 6901.5 |
| Asia | 2022-12-01 | Region | ImportValue | 33611.7 |
| Europe | 2022-12-01 | Region | ImportValue | 7541.8 |
| Oceania | 2022-12-01 | Region | ImportValue | 1399.9 |
| Africa | 2022-12-01 | Region | ImportValue | 414.9 |
4 Visualizations
4.1 Uncover the Singapore Bi-lateral trend over Covid outbreak
We begin with a ribbon plot to illustrate the overall pattern of Singapore international trade over time. The function ggbraid helps distinguish between trade surplus and trade deficit by filling the region between two alternating lines with two different colors.
Step 1. Prepare data for line plot of Singapore
Code
singapore <- wide %>%
subset((Country == "Total Merchandise Imports"|Country == "Total Merchandise Exports")
& Level == "Country") %>%
group_by(MonthYear)
singaporeribbon <- singapore %>%
select(`Country`, `MonthYear`, `Level`, `ImportValue`, `ExportValue`) %>%
gather("Type", "Value", -`Country`, -`MonthYear`, -`Level`) %>%
drop_na()Step 2. Prepare data for line plot of Singapore
Code
hues <- scales::hue_pal()(2)
p <- ggplot() +
geom_line(aes(`MonthYear`, `Value`, linetype = `Type`),
data = singaporeribbon,
show.legend = FALSE) +
labs(title = "Singapore Bi-lateral Trade Trend",
subtitle = "2016-2022",
x = 'Month Year',
y = 'Trade Value'
) +
annotate("segment",
x = as.Date("2020-01-01"),
xend = as.Date("2020-01-01"),
y = 0,
yend = 70000000,
colour = "red") +
annotate("text",
label = "Covid outbreak",
x = as.Date("2020-12-01"),
y=25000000,
color = "red") +
annotate("text",
x = as.Date("2018-06-01"),
y = 60000000, size = 4,
label = "Export > Import",
hjust = 0,
color = hues[1])
p1 <- p+
geom_braid(aes(`MonthYear`,
ymin = `ImportValue`,
ymax = `ExportValue`,
fill = `ImportValue`>`ExportValue`),
show.legend = FALSE,
data = singapore,
alpha = 0.6,
method = 'line')
p1
ggplotly(p)
Tip
As a result of the COVID-19 epidemic, Singapore’s international commerce dropped in 2020
Singapore has a positive trade balance, or a trade surplus, according to the bilateral trend from 2016 to 2022. Prior to the start of the COVID-19 pandemic in 2020, Singapore’s international trade growth was robust from 2016 to 2019, but it began to weaken in 2019. Due to the outbreak of the COVID-19 pandemic in May 2020, the rate drops dramatically before quickly returning.
4.2 Merchandise trade performance with major trading partners
To illustrate the trade balance with all of Singapore’s trading partners, the bubble plot between import and export is chosen. The X-axis shows Singapore’s import value, while the Y-axis shows Singapore’s export value. Each bubble is colored differently depending on the country it represents.
The size of the bubbles indicates the total value of Singapore’s merchandise trade with the trading partner. If the bubble’s center point rises below the line, Singapore’s imports from the trade partner exceed Singapore’s exports. If the bubble’s center point falls beyond the line, Singapore’s exports to the trading partner exceed Singapore’s imports.
The graph is animated to emphasize the fluctuating equilibrium over time. Only major trading partners are depicted on the graph to reflect the effect of the key contributors.
Step 1. Create scatter plot with plotly
Code
# Plot scatter plot
scatter <- wide %>%
subset(Country == "China" | Country == "Malaysia" | Country == "Taiwan" | Country == "Hong Kong" | Country == "Indonesia" )
fig <- scatter %>%
plot_ly(
x = ~`ImportValue`,
y = ~`ExportValue`,
color = ~`Country`,
frame = ~as.character(`MonthYear`, format = "%Y-%m"),
size = ~`Total`,
sizes = c(1,10000),
text= ~paste("Country:",`Country`,
"\nImport Value:", `ImportValue`, " Thousand Dollars",
"\nExport Value:", `ExportValue`, " Thousand Dollars",
"\nTotal:", `Total`,
"\nMonth Year:", `MonthYear`),
hoverinfo = "text",
type = 'scatter',
mode = 'markers'
)Step 2. Set up layout
Code
# Create the diagonal line
dline <- function(color = "steelblue") {
list(
type = "line",
yref = "paper",
xref = "paper",
y0 = 0, y1 = 1,
x0 = 0, x1 = 1,
line = list(color = color, dash="dot")
)
}
# Setup layout
fig <- fig %>%
layout(title = list(text="MERCHANDISE TRADE PERFORMANCE WITH MAJOR TRADING PARTNERS"),
subtitle = "2016-2022",
hoverlabel = list(align = "left"),
shapes = dline(),
legend = list(orientation = "h", y = 1, x = 0),
showlegend = FALSE,
xaxis = list(title="Import Value", range = list(0, 10000000)),
yaxis = list(title="Export Value", range = list(0, 10000000)),
width=650,
height=650
)Step 3. Set up animation
Code
fig <- fig %>%
animation_opts(
500, easing = "linear", redraw = FALSE
)
# Animation slider
fig <- fig %>% animation_slider(
currentvalue = list(prefix = "MONTH-YEAR :", font = list(color="red"))
)
fig
Tip
China’s import and export volume declined sharply in the beginning of 2020
China bubble declines along the diagonal from December 2019 until February 2020. This implies a significant decline in both imports and exports from China within the specified time frame. But, it returned shortly thereafter.
With a similar trajectory to China, the bubble of Malaysia in December 2019 proceeded diagonally downward. Yet, it continued to decline until mid-2020.
In May of 2020, the impact of Indonesia is clearly seen by a 25% decline in overall volume.
Unlike exports to other partners, Singapore’s exports to Hong Kong do not appear to be as affected. However, a faster increase in exports to Hong Kong is observed in 2021.
We employ the ribbon chart once again to assess the impact of Covid-19 on Singapore’s most major trading partners.
Code
braid <- wide %>%
select(`Country`, `MonthYear`, `Level`, `ImportValue`, `ExportValue`) %>%
drop_na() %>%
subset(Country == "China")
ribbon <- gather(braid , "Type", "Value", -`Country`, -`MonthYear`, -`Level`)
braid2 <- wide %>%
select(`Country`, `MonthYear`, `Level`, `ImportValue`, `ExportValue`) %>%
drop_na() %>%
subset(Country == "Malaysia")
ribbon2 <- gather(braid2 , "Type", "Value", -`Country`, -`MonthYear`, -`Level`)
braid3 <- wide %>%
select(`Country`, `MonthYear`, `Level`, `ImportValue`, `ExportValue`) %>%
drop_na() %>%
subset(Country == "Taiwan")
ribbon3 <- gather(braid3 , "Type", "Value", -`Country`, -`MonthYear`, -`Level`)
braid4 <- wide %>%
select(`Country`, `MonthYear`, `Level`, `ImportValue`, `ExportValue`) %>%
drop_na() %>%
subset(Country == "Hong Kong")
ribbon4 <- gather(braid4 , "Type", "Value", -`Country`, -`MonthYear`, -`Level`)
braid5 <- wide %>%
select(`Country`, `MonthYear`, `Level`, `ImportValue`, `ExportValue`) %>%
drop_na() %>%
subset(Country == "Indonesia")
ribbon5 <- gather(braid5 , "Type", "Value", -`Country`, -`MonthYear`, -`Level`)
ggplot() +
geom_line(aes(`MonthYear`, `Value`, linetype = `Type`),
data = ribbon,
show.legend = FALSE) +
geom_braid(aes(`MonthYear`,
ymin = `ImportValue`,
ymax = `ExportValue`,
fill = `ImportValue`>`ExportValue`),
data = braid,
alpha = 0.6,
method = 'line',
show.legend = FALSE)+
labs(title = "Singapore trade balance trend with China",
x = 'Month Year',
y = 'Trade Value'
) +
annotate("rect",
xmin = as.Date("2020-01-01"),
xmax = as.Date("2022-12-01"),
ymin = 0,
ymax = 10000000,
alpha = .1,
fill = "yellow")
ggplot() +
geom_line(aes(`MonthYear`, `Value`, linetype = `Type`),
data = ribbon2,
show.legend = FALSE) +
geom_braid(aes(`MonthYear`,
ymin = `ImportValue`,
ymax = `ExportValue`,
fill = `ImportValue`>`ExportValue`),
data = braid2,
alpha = 0.6,
method = 'line',
show.legend = FALSE)+
labs(title = "Singapore trade balance trend with Malaysia",
x = 'Month Year',
y = 'Trade Value'
) +
annotate("rect",
xmin = as.Date("2020-01-01"),
xmax = as.Date("2022-12-01"),
ymin = 0,
ymax = 10000000,
alpha = .1,
fill = "yellow")+
annotate("text",
x = as.Date("2021-01-01"),
y = 2500000, size = 4,
label = "Import > Export",
hjust = 0,
color = hues[2])
ggplot() +
geom_line(aes(`MonthYear`, `Value`, linetype = `Type`),
data = ribbon3,
show.legend = FALSE) +
geom_braid(aes(`MonthYear`,
ymin = `ImportValue`,
ymax = `ExportValue`,
fill = `ImportValue`<`ExportValue`),
data = braid3,
alpha = 0.6,
method = 'line',
show.legend = FALSE)+
labs(title = "Singapore trade balance trend with Taiwan",
x = 'Month Year',
y = 'Trade Value'
) +
annotate("rect",
xmin = as.Date("2020-01-01"),
xmax = as.Date("2022-12-01"),
ymin = 0,
ymax = 10000000,
alpha = .1,
fill = "yellow")+
annotate("text",
x = as.Date("2019-01-01"),
y = 1000000, size = 4,
label = "Import > Export",
hjust = 0,
color = hues[2])
ggplot() +
geom_line(aes(`MonthYear`, `Value`, linetype = `Type`),
data = ribbon4,
show.legend = FALSE) +
geom_braid(aes(`MonthYear`,
ymin = `ImportValue`,
ymax = `ExportValue`,
fill = `ImportValue`>`ExportValue`),
data = braid4,
alpha = 0.6,
method = 'line',
show.legend = FALSE)+
labs(title = "Singapore trade balance trend with Hong Kong",
x = 'Month Year',
y = 'Trade Value'
) +
annotate("rect",
xmin = as.Date("2020-01-01"),
xmax = as.Date("2022-12-01"),
ymin = 0,
ymax = 10000000,
alpha = .1,
fill = "yellow")+
annotate("text",
x = as.Date("2019-01-01"),
y = 9000000, size = 4,
label = "Export > Import",
hjust = 0,
color = hues[1])
ggplot() +
geom_line(aes(`MonthYear`, `Value`, linetype = `Type`),
data = ribbon5,
show.legend = FALSE) +
geom_braid(aes(`MonthYear`,
ymin = `ImportValue`,
ymax = `ExportValue`,
fill = `ImportValue`>`ExportValue`),
data = braid5,
alpha = 0.6,
method = 'line',
show.legend = FALSE)+
labs(title = "Singapore trade balance trend with Indonesia",
x = 'Month Year',
y = 'Trade Value'
) +
annotate("rect",
xmin = as.Date("2020-01-01"),
xmax = as.Date("2022-12-01"),
ymin = 0,
ymax = 10000000,
alpha = .1,
fill = "yellow")+
annotate("text",
x = as.Date("2019-01-01"),
y = 9000000, size = 4,
label = "Export > Import",
hjust = 0,
color = hues[1])
Tip
Singapore Bi-lateral trend with major trading partners
The ribbon chart conveys the same message as the bubble chart. In February 2020, a significant decline in imports and exports between Singapore and China could be recognized. In terms of import and export rates, trade between Singapore and China has been well balanced. In 2021, Singapore will likely export more to China.
Malaysia demonstrates an unexpected tendency. Prior to the start of Covid-19, commerce between Singapore and Malaysia was balanced. Since the Covid-19 outbreak, however, Singapore has had a trade deficit with Malaysia.
Covid-19 appears to have little effect on Singapore imports from Taiwan. Taiwan’s import volume keeps on growing steadily.
Export from Singapore to Hong Kong has a minimal effect, as there is no discernible change in trend, but the export rate is projected to rise in 2021.
Imports and exports with Indonesia decreased significantly at the beginning of 2020.
4.3 Observe trade dynamic of Singapore’s major trading partners in relative
The slope graph allows us to see how each country’s import and export dynamics change in relation to the other countries over time.
Step 1. Prepare data for slope graph
Code
# Import
slopeimport <- wide %>%
subset(Country == "China" | Country == "Malaysia" | Country == "Taiwan" | Country == "Hong Kong" | Country == "Indonesia" ) %>%
mutate(month = month(MonthYear)) %>%
mutate(year = year(MonthYear)) %>%
drop_na() %>%
group_by(Country, year) %>%
summarise(sumyear = sum(ImportValue)) %>%
mutate(Year = factor(year)) %>%
arrange(`sumyear`)
#Export
slopeexport <- wide %>%
subset(Country == "China" | Country == "Malaysia" | Country == "Taiwan" | Country == "Hong Kong" | Country == "Indonesia" ) %>%
mutate(month = month(MonthYear)) %>%
mutate(year = year(MonthYear)) %>%
drop_na() %>%
group_by(Country, year) %>%
summarise(sumyear = sum(ExportValue)) %>%
mutate(Year = factor(year)) %>%
arrange(`sumyear`)Step 2. Create slope plot
Code
# Create slope plot for import
p <- newggslopegraph(dataframe = slopeimport,
Times = `Year`,
Measurement = `sumyear`,
Grouping = `Country`,
Title = "Total Import per Year",
SubTitle = "2016-2022",
Caption = NULL)
p + annotate("rect",
xmin = "2020",
xmax = "2022",
ymin = -1,
ymax = 600000,
alpha = .1,
fill = "yellow")
# Create slope plot for export
p2 <- newggslopegraph(dataframe = slopeexport,
Times = `Year`,
Measurement = `sumyear`,
Grouping = `Country`,
Title = "Total Export per Year",
SubTitle = "2016-2022",
Caption = NULL)
p2 + annotate("rect",
xmin = "2020",
xmax = "2022",
ymin = -1,
ymax = 600000,
alpha = .1,
fill = "yellow")
Tip
Trade dynamic of Singapore’s major trading partners in relative
There is no change in rank for import. China has consistently ranked #1 in terms of imports. Singapore has expanded its imports from three nations, including China, Malaysia, and Taiwan, beginning in 2020.
Likewise, there is no change in rank on the export side. China has continuously topped the exporting rankings. Similar to Hong Kong, although China’s export rate slowed in 2019, growth accelerated in 2021. Malaysia and Indonesia’s export volumes dropped drastically in 2020 before rebounding in 2021.
4.4 Observing the dynamic of trade balance based on location
Step 1. Prepare data for Choropleth map
Code
data("World")
map <- World %>%
select(iso_a3, name, sovereignt, geometry)
map$name <- as.character(map$name)
map$sovereignt <- as.character(map$sovereignt)Step 2. Create animated map
Code
data_map_area <- map %>%
full_join(wide, by = c('sovereignt' = 'Country')) %>%
drop_na()
tmap_mode("view")
choropleth <- tm_shape(data_map_area) +
tm_polygons("Diff") +
tm_facets(along = "MonthYear", free.coords = FALSE)
tmap_animation(choropleth , filename = "choropleth.gif", delay = 50)
4.5 Monitoring the relative trade balance dynamics of Singapore’s major trading partners
Heatmaps allow us to see the group’s auto-correlation and compare it to another group at the same time.
Step 1. Prepare data by selecting top difference between import and export
Code
heatmap <- wide %>%
drop_na() %>%
group_by(Country) %>%
mutate(totaldiff = sum(Diff)) %>%
arrange(totaldiff) %>%
subset(Country == "China" | Country == "Malaysia" | Country == "Taiwan" | Country == "Hong Kong" | Country == "Indonesia" ) Step 2. Plot heatmap
Code
p <- heatmap %>%
ggplot(aes(x = MonthYear, y = reorder(Country,totaldiff), fill= Diff)) +
geom_tile_interactive(tooltip = c(paste( "Country:", heatmap$Country,
"\n Import:", heatmap$ImportValue,
"\n Export:", heatmap$ExportValue,
"\n Balance:" , -heatmap$Diff,
"\n Month:", heatmap$MonthYear))) +
scale_fill_distiller(palette = "Spectral") +
theme_ipsum() +
geom_tile() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=2))+
labs(title = "Relative trade balance dynamics of Singapore's major trading partners", x="", y="") +
annotate("segment",
x = as.Date("2020-01-01"),
xend = as.Date("2020-01-01"),
y = 0,
yend = 6,
colour = "red") +
annotate("text",
label = "Covid outbreak",
x = as.Date("2020-06-01"),
y=2,
color = "red")
girafe(
ggobj = p,
width_svg = 15,
height_svg = 3
)
Tip
Insights from heatmap
The difference in color tone between Hong Kong and the other countries stand out as the trading relationship between Singapore and Hong Kong is highly surplus. The darker blue color of Hong Kong, which began in early 2021, reflects the quicker rise of export volume in comparison to import volume.
Contrary to Hong Kong, Taiwan reflects the outstanding deficit balance. The gradual darkening of the red shade over time for Taiwan represents the continuing increase of import volume over export.
4.6 Singapore-China trading pattern
Step 1: Deriving month and year fields
Code
cycle <- wide
cycle$month <- month(cycle$`MonthYear`)
cycle$year <- year(cycle$`MonthYear`)Step 2: Extracting the target country
Code
cycle <- cycle %>%
subset(`Country`== "China") %>%
drop_na()Step 3: Plotting the cycle plot
Code
ggplot() +
geom_line(data=cycle, aes(x=year, y=ImportValue, group=month), colour= "steelblue") +
geom_line(data=cycle, aes(x=year, y=ExportValue, group=month), colour= "#ec5954") +
facet_grid(~month) +
labs(title = "Singapore-China trading pattern",
subtitle = "2016-2022") +
ylab("Trading Volume") +
theme(plot.title = element_text(size=15),
axis.text.x = element_text(size = 10, angle = 90),
axis.text.y = element_text(size = 10),
strip.text = element_text(size = 10))
Tip
Singapore-China trade
From the cycle plot, we note that each month follows a similar pattern, with a dip in 2019-2020 and a rise beginning in 2021.
5 Interactive Dashboard
An interactive dashboard has been created to assist users in determining the impact of Covid-19 on Singapore bi-lateral commerce. The dashboard includes user-selected country and month input for time specification.
Code
# Prepare data for dashboard
line <- long %>%
subset(Type == "ImportValue"|Type == "ExportValue")
# Building interactive filters
d <- highlight_key(line)
filter_tools <- htmltools::div(
filter_select(id = "filter",
label = "Select Country",
sharedData = d,
group = ~Country,
multiple=FALSE),
filter_slider(id = "period",
label = "Select period",
sharedData = d,
column = ~year(MonthYear),
width = "100%"),
filter_slider(id = "value",
label = "Select Value",
sharedData = d,
column = ~Value,
width = "100%"),
filter_checkbox(id = "variable",
label = "Select variable",
sharedData = d,
group = ~Type,
inline = FALSE))
vline <- function(x = 0, color = "steelblue") {
list(
type = "line",
y0 = 0, y1 = 1,
yref = "paper",
x0 = x, x1 = x,
line = list(color = color, dash="dot")
)
}
# plotting interactive scatter plot using plotly
p <- plot_ly(data=d,
type= "scatter",
mode= "line",
x= ~MonthYear,
y= ~Value,
color= ~Type,
colors= "Accent",
# fill = 'tonexty',
text= ~paste("Country:",`Country`,
"\nMonth Year:", `MonthYear`,
"\nType:",`Type`)) %>%
layout(title = list(text="<b>Import/Export trend by country</b>"),
hoverlabel = list(align = "left"),
legend = list(orientation = "h", y = 1, x = 0),
shapes = vline("2020"),
xaxis = list(title="Month Year"),
yaxis = list(title="Value"))
gg <- highlight(p, "plotly_selected")
# Using crosstalk bscols to put all 3 elements (filter, scatter plot, datatable) together.
crosstalk::bscols(filter_tools,gg,DT::datatable(d, class= "display",
filter=list(position="top", clear=FALSE),
options=list(pageLength = 10,scrollY = TRUE,
iDisplayLength = 25)),
widths = c(4, 8, 12),
annotations = list(caption = "Data from Department of Statistics, Singapore (DOS)"))Data from Department of Statistics, Singapore (DOS)
Code
function filter_default() {
document.getElementById("filter").getElementsByClassName("selectized")
[0].selectize.setValue("China", false);
}
window.onload = filter_default;6 Conclusion
Covid-19 has had a tremendous impact on the international trade of Singapore. For each country, the impact occurs at a different period and with a distinct magnitude. In 2021, the pattern, however, returned to steady growth.
Code
# Building interactive filters
# d <- highlight_key(ribbon)
# # d2 <- highlight_key(braid)
#
# filter_tools <- htmltools::div(
# filter_select(id = "country",
# label = "Select Country",
# sharedData = d,
# group = ~Country,
# multiple=FALSE),
#
# filter_slider(id = "period",
# label = "Select period",
# sharedData = d,
# column = ~year(MonthYear),
# width = "100%"))
#
# # plotting interactive scatter plot using plotly
# p <- ggplot() +
# geom_line(aes(`MonthYear`, `Value`, linetype = `Type`), data = ribbon)
# # +
# # geom_braid(aes(`MonthYear`,
# # ymin = `ImportValue`,
# # ymax = `ExportValue`,
# # fill = `ImportValue`>`ExportValue`),
# # data = braid, alpha = 0.6) +
# # guides(linetype = "none", fill = "none")
#
#
# gg <- highlight(p, "plotly_selected")
#
# # Using crosstalk bscols to put all 3 elements (filter, scatter plot, datatable) together.
# crosstalk::bscols(filter_tools, gg, widths = c(4, 8))